update all

app.py

@@ -2,25 +2,31 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import MultiTaskLasso, Lasso
 import gradio as gr
+import time
 
 rng = np.random.RandomState(42)
 
 # Generate some 2D coefficients with sine waves with random frequency and phase
-def make_plot(n_samples, n_features, n_tasks, n_relevant_features, alpha):
+def make_plot(n_samples, n_features, n_tasks, n_relevant_features, alpha, progress=gr.Progress()):
+    
+    progress(0, desc="Starting...")
+    time.sleep(1)
+    for i in progress.tqdm(range(100)):
+        time.sleep(0.1) 
 
     coef = np.zeros((n_tasks, n_features))
     times = np.linspace(0, 2 * np.pi, n_tasks)
     for k in range(n_relevant_features):
         coef[:, k] = np.sin((1.0 + rng.randn(1)) * times + 3 * rng.randn(1))
-
+    
     X = rng.randn(n_samples, n_features)
     Y = np.dot(X, coef.T) + rng.randn(n_samples, n_tasks)
-
+    
     coef_lasso_ = np.array([Lasso(alpha=0.5).fit(X, y).coef_ for y in Y.T])
     coef_multi_task_lasso_ = MultiTaskLasso(alpha=alpha).fit(X, Y).coef_
-
+    
     fig = plt.figure(figsize=(8, 5))
-
+    
     feature_to_plot = 0
     fig = plt.figure()
     lw = 2
@@ -34,27 +40,31 @@ def make_plot(n_samples, n_features, n_tasks, n_relevant_features, alpha):
         linewidth=lw,
         label="MultiTaskLasso",
     )
-    plt.legend(loc="upper center")
+    #plt.legend(loc="upper center")
+    plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
+          ncol=3, fancybox=True, shadow=True)
     plt.axis("tight")
     plt.ylim([-1.1, 1.1])
     fig.suptitle("Lasso, MultiTaskLasso and Ground truth time series")
     return fig
-
-
-model_card=f"""
+   
+    
+model_card = f"""
 ## Description
-The multi-task lasso allows to fit multiple regression problems jointly enforcing the selected
-features to be the same across tasks. This example simulates sequential measurements, each task
-is a time instant, and the relevant features vary in amplitude over time while being the same.
-The multi-task lasso imposes that features that are selected at one time point are select
-for all time point. This makes feature selection by the Lasso more stable.
+Multi-task Lasso allows us to jointly fit multiple regression problems  by enforcing the selected features to be the same across tasks. This example simulates sequential measurement. Each task
+is a time instant, and the relevant features, while being the same, vary in amplitude over time. Multi-task lasso imposes that features that are selected at one time point are selected
+for all time points. This makes feature selection more stable than by regular Lasso.
 ## Model
 currentmodule: sklearn.linear_model
 class:`Lasso` and class: `MultiTaskLasso` are used in this example.
 Plots represent Lasso, MultiTaskLasso and Ground truth time series
 """
 
-with gr.Blocks() as demo:
+with gr.Blocks(theme=gr.themes.Glass(primary_hue=gr.themes.colors.gray, 
+               secondary_hue=gr.themes.colors.sky,
+               text_size=gr.themes.sizes.text_lg),
+               css=".gradio-container {background-color: zinc }") as demo:
+    
     gr.Markdown('''
             <div>
             <h1 style='text-align: center'> Joint feature selection with multi-task Lasso </h1>
@@ -63,19 +73,19 @@ with gr.Blocks() as demo:
     gr.Markdown(model_card)
     gr.Markdown("Original example Author: Alexandre Gramfort <[email protected]>")
     gr.Markdown(
-        "Iterative conversion by: <a href=\"https://github.com/DeaMariaLeon\">Dea María Léon</a>"
+        "Iterative conversion by: <a href=\"https://www.deamarialeon.com\">Dea María Léon</a>"
     )
-    with gr.Row():
-       n_samples = gr.Slider(50,500,value=100,step=50,label='Select number of samples')
-       n_features = gr.Slider(5,50,value=30,step=5,label='Select number of features')
-       n_tasks = gr.Slider(5,50,value=40,step=5,label='Select number of tasks')
-       n_relevant_features = gr.Slider(1,10,value=5,step=1,label='Select number of relevant_features')
-    with gr.Column():
-        with gr.Tab('Select Alpha Range'):
-            alpha = gr.Slider(0,10,value=1.0,step=0.5,label='alpha')
-
+    gr.Markdown("### Please select values and click submit:")
+    
+    with gr.Row().style(equal_height=True):    
+       n_samples = gr.Slider(50,500,value=100,step=50,label='Number of samples')
+       n_features = gr.Slider(5,50,value=30,step=5,label='Features')
+       n_tasks = gr.Slider(5,50,value=40,step=5,label='Tasks')
+       n_relevant_features = gr.Slider(1,10,value=5,step=1,label='Relevant features')
+       alpha = gr.Slider(0,10,value=1.0,step=0.5,label='Alpha Range')
+            
     btn = gr.Button(value = 'Submit')
 
     btn.click(make_plot,inputs=[n_samples,n_features, n_tasks, n_relevant_features, alpha],outputs=[gr.Plot()])
 
-demo.launch()
+demo.queue().launch()